Traditional Predictors of OCB: Reviews and Recommendations for Future Research
Bibliographic record
Abstract
Since their introduction over thirty years ago, organizational citizenship behaviors (OCBs) have received substantial attention in the organizational behavior literature. A large portion of the 2,500 articles on this topic are designed to identify the factors that predict OCBs. The focus of this symposium is on four sets of traditional predictors of OCBs that have garnered substantial attention over the past several decades: personality traits, employee trust, employee perceptions of organizational justice, and leader behaviors. Given the attention paid to these traditional predictors, many researchers may wonder what is left to study in this domains, or question the value of future research linking these predictors and OCBs. The presenters in this symposium will briefly review the history of research in each of these areas, but will focus on discussing potential avenues for future research using these traditional predictors of OCBs. Leadership and OCB: Going Above and Beyond Presenter: Ronald F. Piccolo; U. of Central Florida Presenter: Timothy A. Judge; U. of Notre Dame Presenter: Claudia Buengeler; U. of Amsterdam Organizational Justice and Organizational Citizenship Behavior Presenter: Russell Cropanzano; U. of Colorado, Boulder Presenter: Deborah Elizabeth Rupp; Purdue U. Presenter: Meghan Thornton; The U. of Texas at San Antonio Presenter: Ruodan Shao; U. of Manitoba Personality Traits and Citizenship Behavior: Current Research and Future Directions Presenter: Dan S. Chiaburu; Texas A&M U. Presenter: In-Sue Oh; Fox School of Business, Temple U. Presenter: Sophia Vladimirova Marinova; The U. of Alabama Organizational Citizenship Behavior and Trust: The Double Reinforcing Spiral Presenter: Robert Moorman; Elon U. Presenter: Holly H Brower; Wake Forest U. Presenter: Steven Grover; U. of Otago
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".